Engineering System Modelling and Simulation

At the end of the course, the student will be able to: 1. comprehend the techniques of modeling in the context of hierarchy of knowledge about a system and develop the capability to apply the same to study systems through available software; 2. explain different types of simulation techniques; and 3. simulate models for the purpose of developing and apply software.

Introduction: System, environment, input and output variables, State variables; Static and Dynamic systems; Hierarchy of knowledge about a system and Modeling Strategy. Physical Modeling: Dimensions analysis; Similarity criteria and their application to physical models. Modeling of System with Known Structure: Review of conservation laws and the governing equation for heat, mass and momentum transfer; Deterministic model; distributed parametre models; lumped parametre models in terms of differential and difference equations; state space model, transfer functions block diagram and sub systems, stability of transfer functions, modeling for control. Neural Network Modeling of Systems; Neurons, architecture of neural networks, knowledge representation, learning algorithm. Multilayer feed forward network and its back propagation learning algorithm, Application to complex engineering systems and strategy for optimum output. Modeling Based on Expert Knowledge; Fuzzy sets, Membership functions, Fuzzy Inference systems, Expert Knowledge and Fuzzy Models, Design of Fuzzy Controllers Optimisation and Design of Systems: Summary of gradient based techniques: Non-traditional Optimisation techniques (1) genetic Algorithm (GA)- coding, GA operations elitism, Application using MATLAB. Simulation of Engineering Systems; Monte-Carlo simulation, Simulation of continuous and discrete processes with suitable examples from engineering problems.